Population dynamics modelling with spatial heterogeneity for yellow croaker (Larimichthys polyactis) along the coast of China

Abstract

As one of the top four commercially important species in China, yellow croaker (Larimichthys polyactis) with two geographic subpopulations, has undergone profound changes during the last several decades. It is widely comprehended that understanding its population dynamics is critically important for sustainable management of this valuable fishery in China. The only two existing population dynamics models assessed the population of yellow croaker using short time-series data, without considering geographical variations. In this study, Bayesian models with and without hierarchical subpopulation structure were developed to explore the spatial heterogeneity of the population dynamics of yellow croaker from 1968 to 2015. Alternative hypotheses were constructed to test potential temporal patterns in yellow croaker’s population dynamics. Substantial variations in population dynamics characteristics among space and time were found through this study. The population growth rate was revealed to increase since the late 1980s, and the catchability increased more than twice from 1981 to 2015. The East China Sea’s subpopulation witnesses faster growth, but suffers from higher fishing pressure than that in the Bohai Sea and Yellow Sea. The global population and two subpopulations all have high risks of overfishing and being overfished according to the MSY-based reference points in recent years. More conservative management strategies with subpopulation considerations are imperative for the fishery management of yellow croaker in China. The methodology developed in this study could also be applied to the stock assessment and fishery management of other species, especially for those species with large spatial heterogeneity data.

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References

  1. Beamish R J, Noakes D J, McFarlane G A, et al. 1999. The regime concept and natural trends in the production of Pacific salmon. Canadian Journal of Fisheries and Aquatic Sciences, 56(3): 516–526, doi: https://doi.org/10.1139/f98-200

    Google Scholar 

  2. Blomeyer R, Goulding I, Pauly D, et al. 2012. The Role of China in World Fisheries. Brussels: European Parliament, Directorate General for Internal Policies, Policy Department B: Structural and Cohesion Policies-Fisheries

    Google Scholar 

  3. Bureau of Fisheries and Fishery Administration of Ministry of Agriculture. 1969–2016. China Fishery Statistical Yearbook (in Chinese). Beijing: China Agriculture Press

    Google Scholar 

  4. Caddy J. 1998. A short review of precautionary reference points and some proposals for their use in data-poor situations. Rome: Food & Agriculture Organization

    Google Scholar 

  5. Campbell B, Pauly D. 2013. Mariculture: a global analysis of production trends since 1950. Marine Policy, 39: 94–100, doi: https://doi.org/10.1016/j.marpol.2012.10.009

    Google Scholar 

  6. Cao Ling, Chen Yong, Dong Shuanglin, et al. 2017. Opportunity for marine fisheries reform in China. Proceedings of the National Academy of Sciences of the United States of America, 114(3): 435–442, doi: https://doi.org/10.1073/pnas.1616583114

    Google Scholar 

  7. Chang Y J, Brodziak J, O’Malley J, et al. 2015. Model selection and multi-model inference for Bayesian surplus production models: A case study for Pacific blue and striped marlin. Fisheries Research, 166: 129–139, doi: https://doi.org/10.1016/j.fishres.2014.08.023

    Google Scholar 

  8. Cheng Jiahua, Lin Longshan, Ling Jianzhong, et al. 2004. Effects of summer close season and rational utilization on redlip croaker (Larimichthys pofyactis Bleeker) resource in the East China Sea Region. Journal of Fishery Sciences of China (in Chinese), 11(6): 554–560

    Google Scholar 

  9. Clark J S. 2003. Uncertainty and variability in demography and population growth: A hierarchical approach. Ecology, 84(6): 1370–1381, doi: https://doi.org/10.1890/0012-9658(2003)084[1370:UAVIDA]2.0.CO;2

    Google Scholar 

  10. Denwood M J. 2016. Runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software, 71(1): 1–25

    Google Scholar 

  11. de Valpine P, Hastings A. 2002. Fitting population models incorporating process noise and observation error. Ecological Monographs, 72(1): 57–76, doi: https://doi.org/10.1890/0012-9615(2002)072[0057:FPMIPN]2.0.CO;2

    Google Scholar 

  12. Fisheries Bureau and Yellow Sea Fisheries Headquarters in the Ministry of Agriculture. 1990. Investigation and Regionalization of Fishery Resources in the Yellow Sea and Bohai Sea (in Chinese). Beijing: China Ocean Press

    Google Scholar 

  13. Gelman A, Rubin D B. 1992. Inference from iterative simulation using multiple sequences. Statistical Science, 7(4): 457–472, doi: https://doi.org/10.1214/ss/1177011136

    Google Scholar 

  14. Haddon M. 2011. Modelling and Quantitative Methods in Fisheries. 2nd ed. New York: CRC Press

    Google Scholar 

  15. Jensen A L. 2005. Harvest in a fluctuating environment and conservative harvest for the Fox surplus production model. Ecological Modelling, 182(1): 1–9, doi: https://doi.org/10.1016/j.ecolmodel.2003.08.004

    Google Scholar 

  16. Jiao Yan. 2009. Regime shift in marine ecosystems and implications for fisheries management, a review. Reviews in Fish Biology and Fisheries, 19(2): 177–191, doi: https://doi.org/10.1007/s11160-008-9096-8

    Google Scholar 

  17. Jiao Yan, Cortés E, Andrews K, et al. 2011. Poor-data and data-poor species stock assessment using a Bayesian hierarchical approach. Ecological Applications, 21(7): 2691–2708, doi: https://doi.org/10.1890/10-0526.1

    Google Scholar 

  18. Jiao Yan, Hayes C, Cortés E. 2009a. Hierarchical Bayesian approach for population dynamics modelling of fish complexes without species-specific data. ICES Journal of Marine Science, 66(2): 367–377, doi: https://doi.org/10.1093/icesjms/fsn162

    Google Scholar 

  19. Jiao Yan, Lapointe N W R, Angermeier P L, et al. 2009b. Hierarchical demographic approaches for assessing invasion dynamics of non-indigenous species: An example using northern snake-head (Channa argus). Ecological Modelling, 220(13–14): 1681–1689, doi: https://doi.org/10.1016/j.ecolmodel.2009.04.008

    Google Scholar 

  20. Jiao Yan, Neves R, Jones J. 2008. Models and model selection uncertainty in estimating growth rates of endangered freshwater mussel populations. Canadian Journal of Fisheries and Aquatic Sciences, 65(11): 2389–2398, doi: https://doi.org/10.1139/F08-141

    Google Scholar 

  21. Jiao Yan, O’Reilly R, Smith E, et al. 2016. Integrating spatial synchrony/asynchrony of population distribution into stock assessment models: a spatial hierarchical Bayesian statistical catch-at-age approach. ICES Journal of Marine Science, 73(7): 1725–1738, doi: https://doi.org/10.1093/icesjms/fsw036

    Google Scholar 

  22. Jiao Yan, Reid K, Nudds T. 2006. Variation in the catchability of yellow perch (Perca flavescens) in the fisheries of Lake Erie using a Bayesian error-in-variable approach. ICES Journal of Marine Science, 63(9): 1695–1704, doi: https://doi.org/10.1016/j.icesjms.2006.07.002

    Google Scholar 

  23. Jin Xianshi. 1996. Ecology and population dynamics of small yellow croaker (Pseudosciaena polyactis Bleeker) in the Yellow Sea. Journal of Fishery Sciences of China (in Chinese), 3(1): 32–46

    Google Scholar 

  24. Kang Bin, Liu Min, Huang Xiaoxia, et al. 2018. Fisheries in Chinese seas: What can we learn from controversial official fisheries statistics?. Reviews in Fish Biology and Fisheries, 28(3): 503–519, doi: https://doi.org/10.1007/s11160-018-9518-1

    Google Scholar 

  25. Li Jilong, Cao Kun, Ding Fang, et al. 2017. Changes in trophic-level structure of the main fish species caught by China and their relationship with fishing method. Journal of Fishiery Sciences of China (in Chinese), 24(1): 109–119, doi: https://doi.org/10.3724/SP.J.1118.2017.16164

    Google Scholar 

  26. Li Yan, Jiao Yan. 2015. Evaluation of stocking strategies for endangered white abalone using a hierarchical demographic model. Ecological Modelling, 299: 14–22, doi: https://doi.org/10.1016/j.ecolmodel.2014.11.031

    Google Scholar 

  27. Li Jiuqi, Ye Changchen, Wang Wenbo, et al. 2011. A stock assessment of small yellow croaker by Bayes-based Pella-Tomlinson model in the East China Sea. Journal of Shanghai Ocean Univeristy (in Chinese), 20(6): 873–882

    Google Scholar 

  28. Lin Longshan. 2004. Analysis on extant abundance of small yellow croaker Pseudosciaena polyactis in the East China Sea. Marine Fisheries (in Chinese), 26(1): 18–23

    Google Scholar 

  29. Lin Xinzhuo, Deng Siming, Huang Zhengyi. 1965. Study of population on biometrics of small yellow croaker (Pseudosciaena polyactis Bleeker). In: Zhu Yuanding, Zhu Shuping, eds. Collections of Marine Fishery Resource (in Chinese). Beijing: China Agricultural Press, 84–108

    Google Scholar 

  30. Lin Longshan, Liu Zunlei, Jiang Yazhou, et al. 2011. Current status of small yellow croaker resources in the southern Yellow Sea and the East China Sea. Chinese Journal of Oceanology and Limnology, 29(3): 547–555, doi: https://doi.org/10.1007/s00343-011-0182-8

    Google Scholar 

  31. Lin Longshan, Zheng Yuanjia, Cheng Jiahua, et al. 2006. A preliminary study on fishery biology of main commercial fishes surveyed from the bottom trawl fisheries in the East China Sea. Marine Sciences (in Chinese), 30(2): 21–25, 42

    Google Scholar 

  32. Liu Xiaoshun. 1990. Investigation and Division of the Yellow Sea and Bohai Sea Fishery Resources (in Chinese). Beijing: China Ocean Press

    Google Scholar 

  33. Liu Zunlei, Yan Liping, Yuan Xingwei, et al. 2013. Stock assessment of small yellow croaker in the East China Sea based on multi-source data. Journal of Fishery Sciences of China (in Chinese), 20(5): 1039–1049, doi: https://doi.org/10.3724/SP.J.1118.2013.01039

    Google Scholar 

  34. Matsuda H, Abrams P A. 2006. Maximal yields from multispecies fisheries systems: Rules for systems with multiple trophic levels. Ecological Applications, 16(1): 225–237, doi: https://doi.org/10.1890/05-0346

    Google Scholar 

  35. McAllister M K, Kirkwood G P. 1998. Bayesian stock assessment: a review and example application using the logistic model. ICES Journal of Marine Science, 55(6): 1031–1060, doi: https://doi.org/10.1006/jmsc.1998.0425

    Google Scholar 

  36. Parent E, Rivot E. 2012. Introduction to Hierarchical Bayesian Modeling for Ecological Data. Boca Raton: Chapman and Hall

    Google Scholar 

  37. Pauly D, Belhabib D, Blomeyer R, et al. 2014. China’s distant-water fisheries in the 21st century. Fish and Fisheries, 15(3): 474–488, doi: https://doi.org/10.1111/faf.12032

    Google Scholar 

  38. Plummer M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In: Hornik K, Leisch F, Zeileis A, eds. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). Vienna, Austria: Technische Universität Wien, 1–8

    Google Scholar 

  39. Plummer M. 2016. Rjags: Bayesian Graphical Models using MCMC. https://cran.r-project.org/package=rjags [2016-02-19/2017-04-04]

  40. Plummer M, Best N, Cowles K, et al. 2006. CODA: Convergence diagnosis and output analysis for MCMC. R News, 6(1): 7–11

    Google Scholar 

  41. Prager M H. 1994. A suite of extensions to a nonequilibrium surplus-production model. Fishery Bulletin, 92(2): 374–389

    Google Scholar 

  42. Punt A E, Hilborn R. 1997. Fisheries stock assessment and decision analysis: the Bayesian approach. Reviews in Fish Biology and Fisheries, 7(1): 35–63, doi: https://doi.org/10.1023/A:1018419207494

    Google Scholar 

  43. Quinn T J II, Deriso R B. 1999. Quantitative Fish Dynamics. New York: Oxford University Press

    Google Scholar 

  44. Ren Yiping, Gao Tianxiang, Liu Qun, et al. 2001. Study on the population structure and reproduction of Pseudosciaena plyactis in southern Yellow Sea. Transactions of Oceanology and Limnology (in Chinese), (1): 41–46

  45. Roberts G O, Rosenthal J S. 2001. Infinite hierarchies and prior distributions. Bernoulli, 7(3): 453–471, doi: https://doi.org/10.2307/3318496

    Google Scholar 

  46. Schaefer M B. 1954. Some aspects of the dynamics of populations important to the management of commercial marine fisheries. Bulletin, Inter-American Tropical Tuna Commission, 1(2): 26–56

    Google Scholar 

  47. Shan Xiujuan, Li Zhonglu, Dai Fangqun, et al. 2011. Seasonal and annual variations in biological characteristics of small yellow croaker Larimichthys polyactis in the central and southern Yellow Sea. Progress in Fishery Sciences (in Chinese), 32(6): 7–16

    Google Scholar 

  48. Shan Xiujuan, Jin Xianshi, Dai Fangqun, et al. 2016. Population dynamics of fish species in a marine ecosystem: A case study in the Bohai Sea, China. Marine and Coastal Fisheries, 8(1): 100–117, doi: https://doi.org/10.1080/19425120.2015.1114543

    Google Scholar 

  49. Shan Xiujuan, Sun Pengfei, Jin Xianshi, et al. 2013. Long-term changes in fish assemblage structure in the Yellow River estuary ecosystem, China. Marine and Coastal Fisheries, 5(1): 65–78, doi: https://doi.org/10.1080/19425120.2013.768571

    Google Scholar 

  50. Spiegelhalter D J, Best N G, Carlin B P, et al. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 64(4): 583–639, doi: https://doi.org/10.1111/1467-9868.00353

    Google Scholar 

  51. Szuwalski C S, Burgess M G, Costello C, et al. 2017. High fishery catches through trophic cascades in China. Proceedings of the National Academy of Sciences of the United States of America, 114(4): 717–721, doi: https://doi.org/10.1073/pnas.1612722114

    Google Scholar 

  52. Tanaka K R, Cao Jie, Shank B, et al. 2019. A model-based approach to incorporate environmental variability into assessment of a climatically-influenced commercial fishery: A case study with the American lobster fishery in the Gulf of Maine and Georges Bank. ICES Journal of Marine Science, (fsz024): 1–13

  53. Torre M P, Tanaka K R, Chen Y. 2019. Development of a climate-niche model to evaluate spatiotemporal trends in Placopecten magellanicus distribution in the Gulf of Maine, USA. Journal of Northwest Atlantic Fishery Science, 50: 37–50, doi: https://doi.org/10.2960/J.v50.m721

    Google Scholar 

  54. Wang Jintao, Yu Wwei, Chen Xinjun, et al. 2016. Stock assessment for the western winter-spring cohort of neon flying squid (Ommastrephes bartramii) using environmentally dependent surplus production models. Scientia Marina, 80(1): 69–78

    Google Scholar 

  55. Xiong Ying, Zhong Xiaming, Tang Jianhua, et al. 2016. Migration and population structure characteristics of the small yellow croaker Larimichthys polyactis in the southern Yellow Sea. Acta Oceanologica Sinica, 35(6): 34–41, doi: https://doi.org/10.1007/s13131-016-0844-7

    Google Scholar 

  56. Xu Zhaoli, Chen Jiajie. 2010. Population division of Larimichthys polyactis in China Sea. Chinese Journal of Applied Ecology (in Chinese), 21(11): 2856–2864

    Google Scholar 

  57. Yan Liping, Liu Zunlei, Zhang Hui, et al. 2014. On the evolution of biological characteristics and resources of small yellow croaker. Marine Fisheries (in Chinese), 36(6): 481–488

    Google Scholar 

  58. Zhang Chi, Ye Zhenjiang, Wan Rong, et al. 2014. Investigating the population structure of small yellow croaker (Larimichthys polyactis) using internal and external features of otoliths. Fisheries Research, 153: 41–47, doi: https://doi.org/10.1016/j.fishres.2013.12.012

    Google Scholar 

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Acknowledgements

We gratefully thank Xiaoxiao Liu for helping in data synthesis and equally thank Can Zhou during the modelling process. Writing of this manuscript was improved by the comments from Kindong Richard. We thank the Department of Fish and Wildlife Conservation of the Virginia Polytechnic Institute and State University for the opportunity given to Qiuyun Ma to work on this project under the supervision of Yan Jiao. We also thank the China Scholarship Council who provided the funding for Qiuyun Ma to work at Virginia Tech.

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Correspondence to Ying Xue.

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Foundation item: The National Key R&D Program of China under contract No. 2017YFE0104400; the National Natural Science Foundation of China under contract No. 31772852; the Fundamental Research Funds for the Central Universities under contract Nos 201512002 and 201562030.

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Ma, Q., Jiao, Y., Ren, Y. et al. Population dynamics modelling with spatial heterogeneity for yellow croaker (Larimichthys polyactis) along the coast of China. Acta Oceanol. Sin. (2020). https://doi.org/10.1007/s13131-020-1602-4

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Key words

  • yellow croaker
  • population dynamics
  • Bayesian hierarchical model
  • geographic variation